SoroushVahidi/scidocs huggingface dataset:
Efficient Subspace Segmentation via Quadratic Programming
(2011)

We explore in this paper efficient algorithmic solutions to robust subspace segmentation. We propose the SSQP, namely Subspace Segmentation via Quadratic Programming, to partition data drawn from multiple subspaces into multiple clusters. The basic idea of SSQP is to express each datum as the linear combination of other data regularized by an overall term targeting zero reconstruction coefficients over vectors from different subspaces. The derived coefficient matrix by solving a quadratic programming problem is taken as an affinity matrix, upon which spectral clustering is applied to obtain the ultimate segmentation result. Similar to sparse subspace clustering (SCC) and low-rank representation (LRR), SSQP is robust to data noises as validated by experiments on toy data. Experiments on Hopkins 155 database show that SSQP can achieve competitive accuracy as SCC and LRR in segmenting affine subspaces, while experimental results on the Extended Yale Face Database B demonstrate SSQP’s superiority over SCC and LRR. Beyond segmentation accuracy, all experiments show that SSQP is much faster than both SSC and LRR in the practice of subspace segmentation.
Locality-sensitive hashing scheme based on dynamic collision counting
(2012)

Locality-Sensitive Hashing (LSH) and its variants are well-known methods for solving the c-approximate NN Search problem in high-dimensional space. Traditionally, several LSH functions are concatenated to form a "static" compound hash function for building a hash table. In this paper, we propose to use a base of m single LSH functions to construct "dynamic" compound hash functions, and define a new LSH scheme called Collision Counting LSH (C2LSH). If the number of LSH functions under which a data object o collides with a query object q is greater than a pre-specified collision threhold l, then o can be regarded as a good candidate of c-approximate NN of q. This is the basic idea of C2LSH.
 Our theoretical studies show that, by appropriately choosing the size of LSH function base m and the collision threshold l, C2LSH can have a guarantee on query quality. Notably, the parameter m is not affected by dimensionality of data objects, which makes C2LSH especially good for high dimensional NN search. The experimental studies based on synthetic datasets and four real datasets have shown that C2LSH outperforms the state of the art method LSB-forest in high dimensional space.
Design of the Artificial: lessons from the biological roots of general intelligence
(2017)

Our desire and fascination with intelligent machines dates back to the antiquity’s mythical automaton Talos, Aristotle’s mode of mechanical thought (syllogism) and Heron of Alexandria’s mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.
Clustering Algorithms Applied in Educational Data Mining
(2015)

Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.
Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation
(2011)

In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe how to design artificial potential fields to obtain a formation with the shape of a regular polygon. We provide a formal proof of the asymptotic stability of the sys tem, based on the definition of a proper Lyapunov function. We also prove that our control strategy is not affected by the problem of local minima. Then, we exploit a bijective coordinate transformation to deform the polygonal formation, thus obtaining a completely arbitrarily shaped forma tion. Simulations and experimental tests are provided to va lidate the control strategy.
Advanced analytics: opportunities and challenges
(2009)

Purpose – Advanced analytics-driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics. Design/methodology/approach – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses. Originality/value – The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.
Predicting driver drowsiness using vehicle measures: recent insights and future challenges.
(2009)

INTRODUCTION
Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.


METHOD
The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.


RESULTS
Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.


CONCLUSIONS
Further research is necessary to examine more complex functions, as well as individual differences between drivers.


IMPACT ON INDUSTRY
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
Curved double-layer strip folded dipole antenna for WBAN applications
(2017)

This paper presents an optimal design of strip folded dipole antenna with a stacking technique to stabilize the resonance frequency. The designed double-layer strip folded dipole antenna worked well with the cylindrically curved surface on the human body. While being bent, the antenna will change the extended length in each layer within the stacking flexible copper-clad laminate. In this case, the properties of the antenna will be automatically adjusted. However, the extended length can occur at the contact point in each layer. So, we can choose between two points and four points by selecting different types of the designs. This reason, the performance of the extended length can be added by two and four when being compared to the previous multilayer design and the total layer of the new antenna was only two. The extended length of the antenna resulted in making antenna resonance frequency improvement become more stable. The contribution of proposed antenna was used only two layers of flexible copper-clad laminate that reduces a number of antenna's layers with a folded dipole antenna, which causes the designed less complicated, and the flexibility is advantageous for WBAN applications. The double-layer strip folded dipole antenna is designed for 2.45GHz WBAN application.
The Effect of Synonym Substitution on Search Results
(2016)

Synonyms or other semantic associations can be used in web search in query substitution to improve or augment the query to retrieve more relevant search results. The value of substitution depends on how well the synonyms preserve semantic meaning, as any attrition in meaning can result in semantic drift of query results. Many synonyms are not synonyms in the traditional, thesaurus sense, but are semantic associations discovered automatically from online data, with the risk of semantic drift in substitution. This discovery of synonyms or other semantic associations arises from different methods applied over web search logs, and in this paper we review the candidate synonym pairs of words or phrases generated from three different methods applied over the same web search logs. The suitability of the candidate synonym pairs for the purpose of query substitution is evaluated in an experiment where 68 subjects assessed the search results generated by both the original query and the substituted query. It was found that two of the discovery methods returned significantly worse results with the substitution than were returned by the original query for the majority of queries, with only around 20-22% of substituted queries generating either improved or equally-relevant results. The third method however returned a very similar level of superior results as the original query, and saw over 71% of substituted queries generating either improved or equally-relevant results. These results indicate that even when candidate synonym pairs are confirmed as being semantically associated using other methods, they still may not be suitable for query substitution, depending on the method of synonym discovery.
Millimeter-wave mobile broadband with large scale spatial processing for 5G mobile communication
(2012)

The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3-300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10-100 times better than the current 20MHz LTE-Advanced systems.
Emotion circuits in the brain.
(2000)

The field of neuroscience has, after a long period of looking the other way, again embraced emotion as an important research area. Much of the progress has come from studies of fear, and especially fear conditioning. This work has pinpointed the amygdala as an important component of the system involved in the acquisition, storage, and expression of fear memory and has elucidated in detail how stimuli enter, travel through, and exit the amygdala. Some progress has also been made in understanding the cellular and molecular mechanisms that underlie fear conditioning, and recent studies have also shown that the findings from experimental animals apply to the human brain. It is important to remember why this work on emotion succeeded where past efforts failed. It focused on a psychologically well-defined aspect of emotion, avoided vague and poorly defined concepts such as "affect," "hedonic tone," or "emotional feelings," and used a simple and straightforward experimental approach. With so much research being done in this area today, it is important that the mistakes of the past not be made again. It is also time to expand from this foundation into broader aspects of mind and behavior.
Unsupervised Histopathology Image Synthesis
(2017)

Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with onthe-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods.
Moving object surveillance using object proposals and background prior prediction
(2017)

In this paper, a moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance. A simple background estimation method is used to detect rough images of a group of moving foreground objects. The foreground setting in the foreground will estimate another set of candidate object windows, and the target (pedestrian / vehicle) from the intersection area comes from the first two steps. In addition, the time cost is reduced by the estimated area. Experiments on outdoor datasets show that the proposed method can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.
A SURVEY OF DISTRIBUTED FILE SYSTEMS
(1990)

This paper is a survey of the current state of the art in the design and implementation of distributed file systems. It consists of four major parts: an overview of background material, case studies of a number of contemporary file systems, identification of key design techniques, and an examination of current research issues. The systems surveyed are Sun NFS, Apollo Domain, Andrew, IBM AIX DS, AT&T RFS, and Sprite. The coverage of background material includes a taxonomy of file system issues, a brief history of distributed file systems, and a summary of empirical research on file properties. A comprehensive bibliography forms an important of the paper.
Combining Language and Vision with a Multimodal Skip-gram Model
(2015)

We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.
Computing Lexical Contrast
(2013)

Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually created lexicons focus on opposites, such as hot and cold. Opposites are of many kinds such as antipodals, complementaries, and gradable. Existing lexicons often do not classify opposites into the different kinds, however. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as warm and cold or tropical and freezing. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, A and B, are contrasting, then there is a pair of opposites, C and D, such that A and C are strongly related and B and D are strongly related. (For example, there exists the pair of opposites hot and cold such that tropical is related to hot, and freezing is related to cold.) We will call this the contrast hypothesis.We begin with a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites. We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a Roget-like thesaurus. We show how, using four different data sets, we evaluated our approach on two different tasks, solving “most contrasting word” questions and distinguishing synonyms from opposites. The results are analyzed across four parts of speech and across five different kinds of opposites. We show that the proposed measure of lexical contrast obtains high precision and large coverage, outperforming existing methods.
Word Embedding-based Antonym Detection using Thesauri and Distributional Information
(2015)

This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.
Forensic Analysis of WhatsApp on Android Smartphones
(2013)

ii Acknowledgements The research and writing of this thesis has been one of the most significant academic challenges I ever had to face. Without the support and guidance of the following people, this study would not have been completed. It is to them that I owe my deepest gratitude. I would sincerely like to thank my advisor, Dr. Golden G. Richard III for giving me an opportunity to conduct this thesis research under his excellent guidance. His exceptional knowledge, wisdom and understanding have inspired and motivated me. My professors, Dr. Shengru Tu and Dr. Adlai DePano for being on my thesis committee and mentoring me during my Master's degree in Computer Science at University of New Orleans. I am thankful to my friend Thomas Sires, for initiating my interest in Linux, making sure I keep it up and help me solve technical issues. I appreciate the help from Aisha Ali-Gombe, Joe Sylve and Andrew Case who are on our Information Assurance group, for their contributions in the Information Assurance field and answering my questions. Finally, I would like to thank my Parents and each member in my family and friends for their unconditional love and support. I specially want to thank my husband Mr. Naveen Singh, who has been my core source of strength through every phase of this effort and constantly encouraged me to do my best. His systematic approach and kind feedback towards my thesis has helped me improve. Words cannot express how grateful I am to you. My heartfelt gratitude to my precious daughter Navya Singh, who was born during my Master's program and has spent many days without my complete attention. With her never give up attitude she has taught me perseverance to work around a problem until I succeed. Thank you for all your love my little girl, it kept me going. Abstract Android forensics has evolved over time offering significant opportunities and exciting challenges. On one hand, being an open source platform Android is giving developers the freedom to contribute to the rapid growth of the Android market whereas on the other hand Android users may not be aware of the security and privacy implications of installing these applications on their phones. Users may assume that a password-locked device protects their personal information, but applications may retain private information on devices, in ways that users might not anticipate. In this thesis we will be …
Yet another DSL for cross-platforms mobile development
(2013)

With the growing success of mobility, mobile platforms (iOS, Android, WindowsPhone, etc.) multiply, each requiring specific development skills. Given this situation, it becomes very difficult for software developers to duplicate their apps accordingly. Meanwhile, web-based applications have evolved to be "mobile-friendly" but it appears that this is not a silver bullet: the user experience and the overall quality is still better with native applications. Of course, cross-platform mobile development tools have emerged in recent years. This paper provides a survey of these tools and points out that a full-fledged language for mobile development is highly desirable. Consequently, we present a preliminary work on Xmob, a technology-neutral DSL intended to be cross-compiled to produce native code for a variety of platforms.